Lateral Load-Resisting System Using Mass Timber Panel for High-Rise Buildings
Bibliographic record
Abstract
As global interest in using engineered wood products in tall buildings intensifies due to the ‘green’ credential of wood, it is expected that more tall wood buildings will be designed and constructed in the coming years. This, however, brings new challenges to the designers. One of the major challenges is how to design lateral load resisting systems with sufficient stiffness, strength and ductility to resist strong wind and earthquakes. In this study, a hybrid lateral load resisting system (LLRS) using mass timber on stiff podium was developed for high-rise buildings in accordance with capacity-based design principle. The LLRS consisted of shear walls and a shear core, both made of structural composite lumber, connected with dowel-type connections and heavy-duty HSK (wood-steel-composite) system. The wood-based panel-to-panel interface was designed to be the main energy dissipating mechanism of the system. This proposed system was implemented in the design of a hypothetical 20-storey building. A refined finite element model of the building was developed using general-purpose finite element software, ABAQUS. The wind-induced and seismic response of the building model was investigated by performing linear static and nonlinear dynamic analyses. The analysis results proved that the proposed LLRS using mass timber was suitable for high-rise buildings. This study provides a valuable insight into the structural performance of lateral load resisting system constructed with mass timber panels as a viable option to steel and concrete for high-rise buildings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".